图 书 馆
基于半监督二元多目标张量分解的审查滥用检测Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decompo sition |
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| 课程网址: | http://videolectures.net/kdd2019_yelundur_chaoji_mishra/ |
| 主讲教师: | Anil Yelundur |
| 开课单位: | 亚马逊公司 |
| 开课时间: | 2020-03-02 |
| 课程语种: | 英语 |
| 中文简介: |  电子商务网站上的产品评论和评分可为客户提供有关产品各个方面的详细见解，例如质量，实用性等。由于它们会影响客户的购买决定，因此产品评论已成为卖方滥用的沃土（与评论者勾结），以推广自己的产品或破坏竞争对手产品的声誉。在本文中，我们的重点是通过在产品评论数据上应用张量分解来检测此类滥用实体（卖方和评论者）。尽管张量分解大部分是无监督的，但我们利用当前已知的滥用实体将问题表示为半监督的二进制多目标张量分解。我们的经验表明，与无监督技术相比，我们的多目标半监督模型在检测滥用实体方面具有更高的精度和召回率。最后，我们证明了我们的模型所提出的随机部分自然梯度推断在经验上比随机梯度和在线EM具有更快的收敛速度。 |
| 课程简介: | Product reviews and ratings on e-commerce websites provide customers with detailed insights about various aspects of the product such as quality, usefulness, etc. Since they influence customers’ buying decisions, product reviews have become a fertile ground for abuse by sellers (colluding with reviewers) to promote their own products or to tarnish the reputation of competitor’s products. In this paper, our focus is on detecting such abusive entities (both sellers and reviewers) by applying tensor decomposition on the product reviews data. While tensor decomposition is mostly unsupervised, we formulate our problem as a semi-supervised binary multi-target tensor decomposition, to take advantage of currently known abusive entities. We empirically show that our multi-target semi-supervised model achieves higher precision and recall in detecting abusive entities as compared to unsupervised techniques. Finally, we show that our proposed stochastic partial natural gradient inference for our model empirically achieves faster convergence than stochastic gradient and Online-EM with sufficient statistics. |
| 关 键 词: | 电子商务; 产品评价; 购买决定 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-03-24：zhouxj |
| 最后编审: | 2020-05-25：cxin |
| 阅读次数: | 46 |